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Characteristic Selection with Rough Sets for Web Page Ranking
Objective: The objective is to classify web pages and assign ranking to web pages using feature selection with rough sets and TF_IDF methodology. Proposed Method: Web page ranking is a process to assign position at a particular site appears in the result of web page. A site is said to have a high page ranking when it appear at or near the top of the list of web result. A challenge in web page ranking is to provide relevant information to the user according to query. To finding relevant information from the result set is a tedious process. To obtain a refined result set that contains the URL’s more relevant to the user’s query, so it is essential to rank. For classification purpose, we are using feature reduction method based Rough Set Theory (RST). Application: Feature selection is most essential technique in rough sets as well as the data mining. Attribute selection is a main challenge for expanding the theory and making use of rough set. Findings: The proposed method emphases on the removal of the unnecessary attributes as a way to sort the effective reduct set and framing the core of the attribute set. After successful classification procedure, we have to applying TF_IDF methodology for assign the ranking to the documents.
Core, Data Preprocessing, Data Mining, Feature Selection, Rough Sets Theory (RST), Reduct, Tf-IDF, Text Mining
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